Setting the best training / validation ratio in a Neural Network
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I am using a Neural Network to make a regression, using 10% of data to test. But how can I set the ratio values of training and validation datasets?
3 Kommentare
Greg Heath
am 16 Jan. 2019
FYI: The default ratios are 0.7/0.15/0.15
Do you ave a specific reason for not accepting them?
Greg
Antworten (1)
Greg Heath
am 16 Jan. 2019
Bearbeitet: Greg Heath
am 16 Jan. 2019
1. ALWAYS START WITH 10 DESIGNS USING THE MATLAB DEFAULT!
2. Then evaluate the results to determine what to modify.
3. For regression the default is FITNET. So, look at the codes in
help fitnet
and
doc fitnet
4. They are the same:
[ x, t ] = simplefit_dataset;
net = fitnet(H); % H = 10 hidden nodes
net = train(net,x,t);
view(net)
y=net(x);
perf = perform(net,t,y)
5. Since I don't trust "perform" , I add a normalized mean square error calculation which typically has a range from 0 to 1
NMSE = mse(t-y)/mse(t-mean(t)) % 0 <= NMSE <= 1
7. Search using
Greg NMSE
8. This is related to the familiar Rsquare (coefficient of determination) used in elementary statistics
(See any encyclopedia)
Rsquare = 1-NMSE
9. If successful, the next step is to try to obtain good results with the number of hidden nodes
H < 10
10. Otherwise, increase H.
11. I have a jillion examples in both the NEWSGROUP and ANSWERS.
PS: This format sucks.
Greg
2 Kommentare
Greg Heath
am 16 Jan. 2019
Bearbeitet: Greg Heath
am 16 Jan. 2019
I SEE NO REASON FOR IT'S EXISTENCE.!
My approach is as simple as possible. Typically, I accept all defaults except a double for loop over a non-overfitting number of Hidden nodes and 10 or (RARELY!) 20 sets of random initial weights for each value of H.
I have posted jillions of exmples in BOTH comp.soft-sys.matlab and ANSWERS.
HOPE THIS HELPS.
GREG
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